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Atomically precise Ag4Ni2nanoclusters with 2,4-dimethylbenzenethiol as the ligands were synthesized and characterized as a cocatalyst of SiC for the selective hydrogenation of nitroarenes to arylamine in the presence of NaBH4. The obtained Ag4Ni2/SiC samples exhibited extraordinary catalytic activity, and a self-accelerated catalytic process was observed with the reduction of nitrophenol to aminophenol as the model reaction. Experimental comparison between the Ag4Ni2/SiC samples before and after the catalysis showed that the transformation of Ag4Ni2clusters to polydisperse Ag particles as well as amorphous NiOxon the surface of SiC in the catalysis was the key to their high activity. AIMD calculations revealed that the transformation of Ag4Ni2was driven by the presence of multiple hydrides on the cluster, which induced the detachment of the thiol ligand of the nanoclusters.

Research in medical visual question answering (MVQA) can contribute to the development of computer-aided diagnosis. MVQA is a task that aims to predict accurate and convincing answers based on given medical images and associated natural language questions. This task requires extracting medical knowledge-rich feature content and making fine-grained understandings of them. Therefore, constructing an effective feature extraction and understanding scheme are keys to modeling. Existing MVQA question extraction schemes mainly focus on word information, ignoring medical information in the text, such as medical concepts and domain-specific terms. Meanwhile, some visual and textual feature understanding schemes cannot effectively capture the correlation between regions and keywords for reasonable visual reasoning. In this study, a dual-attention learning network with word and sentence embedding (DALNet-WSE) is proposed. We design a module, transformer with sentence embedding (TSE), to extract a double embedding representation of questions containing keywords and medical information. A dual-attention learning (DAL) module consisting of self-attention and guided attention is proposed to model intensive intramodal and intermodal interactions. With multiple DAL modules (DALs), learning visual and textual co-attention can increase the granularity of understanding and improve visual reasoning. Experimental results on the ImageCLEF 2019 VQA-MED (VQA-MED 2019) and VQA-RAD datasets demonstrate that our proposed method outperforms previous state-of-the-art methods. According to the ablation studies and Grad-CAM maps, DALNet-WSE can extract rich textual information and has strong visual reasoning ability.

Ultrasensitive atomic comagnetometers have proven to be valuable tools for precise measurement and search for new physics. Besides, comagnetometers have potential for inertial navigation and quantum information. Despite their great potential, sensitivity improvement has been limited by different types of noise. To address this issue, the sources and magnitudes of noises are comprehensively analyzed and a suppression method is proposed for the most critical noise source, optical nonorthogonality. The optical nonorthogonality leads to critical transverse pumping effect, thus transverse light shift, and transverse effective magnetic noise. A direct correlation between the optical nonorthogonality and the electronic spin transverse relaxation rate is found, and a method for suppression of the optical nonorthogonality is proposed accordingly. Finally, the optical nonorthogonal noise is suppressed by 3.1 dB. Besides, an increase of 5.2 dB in the scale factor is achieved. With the combined effect of noise suppression and scale factor enhancement, the resulting inertial rotation sensitivity of $2.4 \times 10^{-8}$ rad/s/Hz $^{1/2}$ in the frequency range from 1 to 3 Hz is among the most sensitive comagnetometers, which opens up promising opportunities for new physics measurements, such as the search for ultralight dark matter and the fifth force.

The Transformer has been successfully used in medical image segmentation due to its excellent long-range modeling capabilities. However, patch segmentation is necessary when building a Transformer class model. This process ignores the tissue structure features within patch, resulting in the loss of shallow representation information. In this study, we propose a Heterogeneous Swin Transformer with Multi-Receptive Field (HST-MRF) model that fuses patch information from different receptive fields to solve the problem of loss of feature information caused by patch segmentation. The heterogeneous Swin Transformer (HST) is the core module, which achieves the interaction of multi-receptive field patch information through heterogeneous attention and passes it to the next stage for progressive learning, thus complementing the patch structure information. We also designed a two-stage fusion module, multimodal bilinear pooling (MBP), to assist HST in further fusing multi-receptive field information and combining low-level and high-level semantic information for accurate localization of lesion regions. In addition, we developed adaptive patch embedding (APE) and soft channel attention (SCA) modules to retain more valuable information when acquiring patch embedding and filtering channel features, respectively, thereby improving model segmentation quality. We evaluated HST-MRF on multiple datasets for polyp, skin lesion and breast ultrasound segmentation tasks. Experimental results show that our proposed method outperforms state-of-the-art models and can achieve superior performance. Furthermore, we verified the effectiveness of each module and the benefits of multi-receptive field segmentation in reducing the loss of structural information through ablation experiments and qualitative analysis.